Energy Policy 122 (2018) 260–272
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A review of EROEI-dynamics energy-transition models Craig D. Rye , Tim Jackson ⁎
Centre for the Understanding of Sustainable Prosperity (CUSP), University of Surrey, 388 Stag Hill, Guildford GU2 7XH, United Kingdom
A R T I CL E I N F O
ABSTRAC T
Keywords:
The need for an environmentally sustainable economy is indisputable but our understanding of the energyeconomy interactions (dynamics) that will occur during the transition is insu fficient. This raises fascinating questions on the future of economic growth, energy technology mix and energy availability. The crucial interactions between energy and economy systems can be usefully described in terms of the Energy Returned on Energy Invested (EROEI) metric (the energy cost of primary energy production). Multiple authors have used this metric to explore the behaviour of the economy over the transition to lower carbon energy sources. The following text is a review of energy-economy models that incorporate the EROEI metric. In particular, the EROEIdynamics literature is found to describe a common set of dynamics associated with the transition to lower EROEI primary energy resources. These include: the rising resource-cost of primary energy production, the short-term misallocation of resources, the short-term overproduction of energy and the potential decline in economic stability. The literature can be divided into groups of related models. Following the review, a number of key areas for additional work are identi �ed and discussed.
EROEI Net-Energy Model Transition Dynamics Biophysical
1. Introduction Introduction
for society.
The Energy Returned on Energy Invested (EROEI; aka e fficiency) of primary energy production has declined markedly over recent decades (Murph Murphy, y, 2014 2014). ). Furthermore, the EROEI of primary production is projected to decline further in the early half of the current century, associated with the continuing depletion of fossil fuel reserves and the transition away from conventional fossil fuels (IPCC, ( IPCC, 2014; Sterman, 1982; Gagnon et al., 2009; Grandell et al., 2011; Murphy and Hall, 2010). 2010 ). For example, Guilford et al., (2011) �nd that the EROEI for United States Oil and Gas has declined by around a half, between 1930 and present. The continuing decline of EROEI is a signi �cant academic and political concern posing impacts on energy futures and the dynamics of the transition to a low carbon economy. EROEI is estimated as the energy produced by an energy-gathering activity divided by the energy required for that production (Eq. (1) (1)). ). The energy produced by energy gathering activity is refered to here as primary energy. Typical EROEI values are provided in Table 1. 1. A similar useful metric of primary energy e fficiency is Net Energy (NE). NE is estimated as the energy produced by primary energy minus the energy required for that production (Eq. (2) (2)). ). Both the EROEI and NE metrics indicate the efficiency of energy production. EROEI is a particularly interesting term because its name explicitly refers to the energy that is ‘ used up’ or ‘ invested’ in the process of producing useful energy
EROEI
⁎
NE
=
Energy Returned Returned =
Energy Returned Returned
−
Energy Invested Invested
(1) (2)
Many of the core insights of the EROEI (or NE) metric are intuitive but poorly accounted for. For example, an energy carrier (such as gasoline) must provide more energy when it is used than it requires in production. Otherwise, it would not make ‘ economic sense’ to produce it. The greater the energy return, or net energy, the greater the positive eff ect ect on the economy. Unfortunately, this kind of argument is often neglected. For example, green energy subsidies typically support biofuels and wind energy equally, however, wind power usually provides signi�cantly greater energy and economic economic return. return. Therefore Therefore subsidies are often biased towards low EROEI renewables. EROEI provides a powerful powerful approach for examining the e fficiency of primary primary energy energy generation technologies. Charles Hall and collaborators provide many early works on EROEI in economics (e.g. Hall and Cleveland, 1981; 1981 ; Cleveland et al., 1984; 1984 ; Hall et al., 2008; 2008 ; Hall et al., 2009). 2009 ). Notably, Murphy Notably, Murphy and Hall (2010) highlight that the relationship between Net Energy and the EROEI is exponential where NE declines rapidly as EROEI falls below approximately ~10:1. The relationship between EROEI and NE is commonly referred to as the ‘Net Energy cli ff ’ (see Fig. 1). 1). This relationshi relationshipp
Corresponding author.
[email protected] (C.D. (C.D. Rye). E-mail address:
[email protected] https://doi.org/10.1016/j.enpol.2018.06.041 Received 8 November 2017; Received in revised form 26 June 2018; Accepted 27 June 2018 0301-4215/ © 2018 Elsevier Ltd. All rights reserved.
Energy Invested Invested
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curve models. Each model is described (typically in 3 paragraphs) in terms of its novelty, technical detail and main � ndings. Section ndings. Section 4 pro4 provides a discussion discussion and Section and Section 5 gives 5 gives concluding remarks.
Table 1
Typical EROEI values (derived from Hall and Klitgaard, 2011). 2011 ). Pri Primary mary energ nergyy prod produc ucti tion on tech techno nolo logi gies es
Appr Approx oxiimate mate EROE EROEII
Hydropower Historic oil and gas Coal Current global oil and gas Wind (no storage) Nuclear Current US oil and gas Solar (no storage) Unconventional oil and gas Biofuels
> 1 0 0 :1 1 0 0 :1 8 0 :1 2 0 :1 20:1 1 5 :1 1 0 :1 1 0 :1 5 :1 2 :1
2. Numerical Numerical models of EROEI-dyna EROEI-dynamics mics
In this section, we review the history of energy-economy models that speci�cally explore EROEI dynamics. Emphasis is given to seminal works. There are unavoidably a number of limitations to this review. During the development of the literature, models are often documented inconsistently therefore it is challenging to provide a consistent outline of technical detail. Further, choosing the boundaries of the review is challenging. For example, this review does not explore the broader Integrated Assessment Modelling (IAM) literature. The accuracy of the IAM literature in simulating simulating EROEI-dynamics EROEI-dynamics is debated debated (e.g. (e.g. Dale et al., 2013) 2013 ) and this discussion is beyond the scope of this review. However, some of the EROEI-dynamics models may be considered as belonging to the IAM literature and vice-versa. This review particularly emphasises research that directly refers to EROEI. 2.1. Early Models (~1970 to 1980)
World3 is perhaps the earliest model to (implicitly) simulate EROEI dynamics (Meadows (Meadows et al., 1972). 1972 ). It is associated with the Limits to Growth study (Meado (Meadows ws et al., 1972) 1972) and was designed to explore the integrated environmental challenges facing the global economy in the 21st century. The model was derived from the earlier work of Forester (1970) on Industrial Dynamics and has had a de �ning impact on the following energy-economy-environment debate. The World3 model can be technically described as a globally aggregated, system dynamics framework, where well-chosen simpli �ed equations are used to represent large complex systems. The model implicitly simulates EROEI-dynamics by assuming that the cost of extracting non-renewable resources increases as resources are depleted. Therefore Therefore as the model consumes consumes non-renewabl non-renewablee resources, resources, their cost increases and this drives a downward pressure on economic growth. However, the model does not explicitly simulate energy systems. The main results results (or dynamics) of World3 are relatively relatively consistent consistent for a range of reasonable model parameters. The ‘standard run’ of World3 suggests that the current growth trend of the global economy is unsustainable; leading to the encroachment of environmental limits and ultimately ‘overshoot and collapse ’ (Fig. 2). 2). In this case, industrial output and food production production peak around 2015 and decline thereafter. thereafter. Population and pollution peak around 2030 and decline thereafter. The simulation stabilises around the beginning of the 22nd century with a global population of around a half of the peak value. However, accounting for the models ’ uncertainty, its precise predictions are less important than the underlying dynamics (Jackson ( Jackson and Webster, 2016). 2016 ).
Fig. 1. The relationship between Net Energy (expressed as a percentage) Energy
Return on Energy Invested: ‘ the net-energy cli ff ’ (Murphy 2013).
suggests that advanced economies require EROEI values of at least 5 to maintain key infrastructure and avoid economic decline. From a similar argument, it is suggested that the Net Energy cli ff could could play a key role in the growth rate of an economy. Following the work of Hall and Murphy, numerous authors have conducted empirical analyses of the relationship between EROEI and other economic indicators. For example, Heun and de Wit (2012) King and Hall (2011) and and Brandt (2017) �nd interesting relationships between EROEI and energy energy prices. prices. Heun and de Wit (2012) suggest that these relationships may break down as EROEI becomes small. Hall small. Hall et al. (2008) suggest (2008) suggest that during periods of energy shortage (i.e. low EROEI, such as the 1970s ‘energy crisis ’ or the rising prices of 2000 –2007) discretionary spending typically declines. Finally, Rubin (2012) and Hamilton (2009) use (2009) use empirical analysis to argue that the rising energy prices (declining EROEI) of 2000 –2007 contributed contributed to the recent 2008 �nancial crisis. Empirical works therefore typically suggest an inverse relationship between EROEI and energy prices, and a link between EROEI and economic health. Numerical models have been developed to explore the related set of system behaviours behaviours accompanying accompanying the transition transition towards lower EROEI (e.g. Sterman, (e.g. Sterman, 1982; 1982; Hounam, 1979; 1979; Baines and Peet, 1983; 1983 ; Bodger and Baines, 1988). 1988 ). These models typically simulate the behaviour of the energy-economy during a period of declining resource quality, or energy technology technology transition. transition. The emergence, emergence, nuance, evolution and association of these EROEI-dynamics models are the subject of this review. The structure of this review is as follows: Section 2 outlines the history of EROEI-Dynamics models. Section 2.1: 2.1: discusses formative work (1970–1980). Section 1980). Section 2.2: 2.2: covers a period of rapid development (1980–2000). Section Section 2.3 2.3:: high highli ligh ghts ts the the most most rece recent nt lite litera ratur turee (2000–2017). Section 2017). Section 3 outlines 3 outlines the closely related �eld of resource-
Fig. 2. The distribution of core variables for the World3 ‘ Standard Run ’, taken from (Meadows (Meadows et al., 1972 ).
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Shortly after publication World3 was heavily criticised (see Hall and Day, 2009; 2009 ; Bardi, 2011; 2011 ; Jackson and Webste Webster, r, 2016 for 2016 for more details). But the results of World3 were revisited by Turner (2008), (2008), who concluded that the behaviour of the global economy 1972 –2008 agrees well with the ‘standard run ’ scenario. Furthermore, World3 was recently re-calibrated by Pasqualino by Pasqualino et al. (2015), (2015) , who argue that recent improvements improvements in pollution pollution and food productivition productivition may have delayed delayed but not negated Meadows et al. (1972) prediction of ‘overshoot and collapse’. Following Following World3, Roger Naill and collaborator collaboratorss (in association with the U.S. Department of Energy; Naill, 1973) 1973) developed a progression of �ve models, COAL 1 and 2, FOSSIL 1 and 2, and IDEAS. Unfortunately, these models are sparsely documented. However, these models are well cited cited and conside considered red in�uential uential on follow following ing work (Sterman Sterman,, 1982 1982). ). COAL and FOSSIL are perhaps the earliest System Dynamics models to incorporate energy market dynamics, investment time lags and price elasticities. elasticities. Later Later iterations iterations of the group, such as FOSSIL2 FOSSIL2 and and IDEAS were used by the U.S. Department of Energy between 1973 and 1995, as policy design and testing tools (Qudrat-Ullah, ( Qudrat-Ullah, 2013). 2013). From a technical perspective, early iterations of COAL follow an aggregated ( ‘top-down’) approach similar to World3. However, later iterations of FOSSIL emphasise greater detail in primary energy production (‘bottom-up’ approach). FOSSIL 2 and IDEAS may be classed principally as bottom-up models (Qudra (Qudrat-Ulla t-Ullah, h, 2013 2013), ), where each sector, and often subsectors are described explicitly with individual behavioural equations. Following World3, the COAL-FOSSIL studies, the STER (System, Time, Energy and Resources) Resources) model, documented by Hounam (1979), (1979), presents an innovative EROEI-dynamics approach. STER is one of the earliest models to explore the behaviour of the energy sector during a period of resource depletion, and therefore also one of the �rst to directly focus on EROEI-dynamics. It may also be one of the earliest examples of a ‘biophysical’ EROEI model, where all capital �ows and production are de �ned in terms of energy. In addition, the research question posed by STER is novel; STER is designed to determine the maximum possible (resource constrained) growth rate that an economy can achieve in a given time period. In this regard, the model design avoids the uncertainty of many parameters by framing results in terms of the maximum maximum econom economic ic potent potential ial.. Unfort Unfortuna unatel tely, y, STER STER is only only documented by a single conference paper where it is described as a pilot study (Hounam, (Hounam, 1979). 1979). It is difficult to discuss the technical detail of STER because of sparse documentation documentation.. The structure is shown in Fig. in Fig. 3. 3. STER is a simpli�ed, aggregated, top-down model with a production function articulated in terms of energy, capital, labour, and technology. The model simulates EROEI dynamics by assuming that the demand for resources by the primary energy sector increases (i.e. the resource cost of primary energy production increases) as energy resources are depleted. The results of STER suggest that, if energy use is held constant, constant, the relative resource consumption of the energy sector must increase as resources are depleted, so as to keep up with demand. The rising cost of energy production then leads to the crowding out of (non-energy) industrial production and the growth of the energy sector relative to the non-energy industry (Fig. ( Fig. 4). 4). This (relative) expansion of the energy sector during the transition to low EROEI is an important process discussed by the EROEI-dynamics literature. Direct Directly ly follow following ing World3 World3 and the COAL-F COAL-FOSSI OSSILL studies studies,, John John Sterman of the MIT System Dynamics group developed a seminal energy-e ergy-econ conomy omy model, model, referr referred ed to here here as the Sterma Stermann model model (e.g. (e.g. Sterman, Sterma n, 1982 1982). ). In line with STER, the Sterman model is one of the earliest models designed to speci �cally simulate the energy-econ energy-economy omy dynamics associated with a transition from high to low EROEI primary energy. Furthermore, the model provides one of the earliest direct references to EROEI-dynamics (Sterman, (Sterman, 1982; 1982; Section 2.1 para. 8). A diagram of the structure of the Sterman model is shown in Fig. 5. 5. The Sterman model is a globally aggregated, top-down, system
of Gilliland (1978) theory, and Fig. 3. Schematic illustration of the structure of Gilliland the STER model (Hounam, ( Hounam, 1979). 1979 ).
dynamics framework that uses a nested, constant elasticity of substitution production function in technology, capital, labour and energy. The model characterises the energy sector in terms of two aggregated technology groups, conventional and unconventional energy. This simpli�cation supports the inclusion of complex economic detail and allows the examination of many interesting energy-economy dynamics. For example, the model includes in �ation, monetary policy and international trade. It also provides a complex representation of investment and capital depreciation. Conversely, for simplicity, the model excludes many interesting factors that may play important roles in energy-tran energy-transition sition dynamics, such as business business inventories inventories and environenvironmental interactions. The Sterman model simulates EROEI-dynamics using two key assumptions. In principle, it assumes that the cost of producing conventional energy increases as reserves are depleted (whereas the reserves of unconventional energy are in �nite). Further, it assumes that the initial EROEI of conventional fossil fuels is greater than the EROEI of unconventional fossil fuels. Therefore, as the economy consumes conventional energy or transitions from conventional to unconventional energy energy,, the resour resource ce cost cost of energy energy produc production tion increa increases ses.. This This increasing demand for resources by the energy sector ‘crowds out’ investment in the non-energy economy and provides a downward pressure on economic growth. It is noted that the EROEI-dynamics of the model follow from similar core assumptions assumptions made by the World3. World3. The conclusions of Sterman Sterman (1982) emphasises three key energyeconomy dynamics: 1
1. As previously previously stated, the depletion depletion of conventional conventional energy energy leads to increasing resource requirements for the energy sector, leading to a downward pressure on economic growth by ‘crowding crowding out’ investment in the non-energy economy. 2. Energy price increases are expected to drive an increase in economywide energy efficiency, however, it is argued that this will occur more slowly than investment in energy production. Therefore, in the short-term energy may be over-produced and capital misallocated. In the long run, run, the misalloca misallocatio tionn of capital capital will constit constitute ute inefficiency. 3. Due to the capital-intensity of energy production, signi �cant investment will be required in the short-run at the start of the transition; sition; the initial initial cost cost of transi transition tion will will facili facilitate tate a shortshort-ter term m downward pressure on economic growth and may slow the transition.
The constant elasticity of substitution (CES) production function is a simple aggregate model of production (Solow, ( Solow, 1956). 1956 ). The function is characterised by constant elasticity ’s for its factors (e.g. capital and labour) regardless of production scale. The role of energy in production is well discussed; authors such as Cantillon as Cantillon (1730) and (1730) and Georgescu-Roegen Georgescu-Roegen (1971) make (1971) make notable contributions. Early modelling studies that incorporate energy as a factor of production include: Tintner clude: Tintner et al., 1974; 1974 ; Hudson and Jorgen Jorgenson, son, 1974 ; Berndt and Wood, 1979). 1979 ). 1
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(1979) . Fig. 4. STER model output. Left: Sectorial percentage of capital. Right: capacities of energy sectors. Hounam (1979).
Fig. 6. An example of output from the Sterman model, highlighting the energy
transition envisioned by Sterman (1981). 2.2. Intermediate models (~1980 to 2000)
Following the Sterman model and the STER model, Baines and Peet (1983),, and Bodger (1983) and Bodger and Baines (1988) developed a system dynamics model to explore the role of historical energy transitions (e.g. from biomass, to coal, to oil and gas) in long-run (inter-decadal) economic variability (e.g. Kondratie (e.g. Kondratieff , 1979). 1979). This is referred to here as the BPB model. A schematic of the BPB model and an illustration of the energy availability over recent energy transitions are shown in Figs. 7 and 8 respectively (Baines (Baines and Peet, 1983). 1983 ). In a technical regard, the BPB model is a globally aggregated, topdown, biophysical approach that uses constant values for the EROEI of energy technologies. The model simulates historical energy transitions by assuming that the economy has a preference to consume the highest EROEI resources available. Therefore economies typically transition to higher EROEI resources as they become available. The model contains a similar level of energy production detail to the STER model. The BPB model was not thoroughly calibrated to historical data ( Dale, et al., 2012). 2012 ). Furthe Furthermo rmore, re, it does does not appear appear to include include a comple complexx representation of the economy such as the Sterman model. The results of the BPB model suggest that EROEI-dynamics have
1982 ). Fig. 5. Schematic illustration of the Sterman model structure ( Sterman, 1982).
The distribution of energy production over the transition as pro jected by Sterman is i s illustrated by Fig. by Fig. 6. 6. Sterman concludes: ‘ the road to the economy economy freed freed from from depend dependenc encee on non-re non-renew newabl ablee energy energy sources is likely to be quite long and rocky ’ (Sterman, (Sterman, 1982, 1982, page 352). 263
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CORECCO CORECCO explicitly explicitly simulates EROEI dynamics dynamics at an aggregate level. The standard implementation implementation of CORECCO CORECCO suggests suggests that industrial industrial output of the global economy will peak in the early half of the current current century and decline thereafter. In the late 90s, Fiddaman (1997) (1997) developed the Feedback Rich Energy Economy model (FREE) in association with the MIT System Dynamics group that draws directly from World3 and the Sterman model (Fig. (Fig. 10). 10). FREE is designed to highlight the role of complex nonlinear feedbacks in energy-economy systems, particularly over a period of energy transition. Furthermore, it is designed to explore the response of the energy-economy to policy proposals. The FREE model simulates a large range of economy-energy-environment interactions, it therefore can be considered an Integrated Assessment Model, as well as an EROEI-dynamics model. The economy structure of FREE is derived from the highly cited DICE Integrated Assessment Model (Nordhaus, ( Nordhaus, 1992). 1992 ). The energy systems and energyeconomy coupling of FREE is derived from the Sterman model (e.g. Sterman, Sterma n, 1982 1982). ). FREE uses a Cobb-Douglas production function with factors in Labour, Capital, Energy and Technology. Unlike the Sterman and World3 models, the population and total factor productivity are estimated exogenously. FREE explicitly accounts for EROEI dynamics and resource constraints, as well as climate interactions ( Fiddaman, 1997, 1998). 1998). The ‘business as usual’ results of the FREE model suggest that the net energy output of the global economy will decline between 2020 and 2045. However, However, unlike the Sterman Sterman or World3 models, models, the GDP of the economy does not appear to decline. This distinction for GDP highlights a potential rigidity in the model that may be associated with exogenous parameters, such as population or total factor productivity growth. Similar to Sterman to Sterman (1982), (1982), Fiddaman argues: ‘Much of the harm from depletion actually arises from the di fficult period of transition away from oil and gas, rather than from the long-run e ff ects ects of losing the services of those fuels ’. This is due to the cost of building new and retiri retiring ng old energy energy systems systems (pg. (pg. 15, Fiddaman, Fiddaman, 1997 1997). ). Fiddaman Fiddaman therefore additionally supports Sterman (1982)’s emphasis on the role of the misallocation of capital during the transition, both in the energy industry and in the none-energy industry. Fiddaman argues ‘Depletion leads to suboptimal capacity utilization in the goods producing sector, because energy prices are far from the levels for which the capital stock was designed. In extreme scenarios, when depletion suddenly becomes severe, a near-shutdown of the economy is possible. ’ (pg. 15, Fiddaman, 15, Fiddaman, 1997). 1997 ). Fiddamen argues that a depletion tax on oil and gas improves the economic stability of the transition by increasing the price of oil and gas earlier; therefore, by spreading the economic cost and risk of systemic failure.
schematic showing the BPB model view of EROEI dynamics ( Bodger Fig. 7. A schematic and Baines, 1988). 1988 ).
Fig. 8. A schematic showing the distribution of capital between competing energy technologies technologies over a series of transitions, transitions, as Envisioned Envisioned by Bodger and Baines (1988), (1988) , associated with the BPB Model.
played a plausible role in long-run (inter-decadal) economic variability, particularly in association with energy technology transitions. This argument is supported by the work of other notable authors, such as Schumpeter (e.g. Schumpeter, 1939), 1939), Sterman Sterman (1985) and (1985) and King et al. (2015).. (2015) In the early 90s Malcolm Slesser and colleagues developed the Enhancement of Carrying Capacity Options (ECCO) modelling framework in association with UNESCO (e.g. Slesser, (e.g. Slesser, 1992). 1992 ). ECCO was originally developed to assist supply-side growth in low-GDP nations and has been applied to a range of economies including Kenya ( Owino, 1991), 1991 ), China (Wenhua (Wenhua,, 1991; Xiaohui, 1995) 1995) and the Netherlands (Noorman, 1990, 1995). 1995 ). ECCO is a biophysical system dynamics approach that draws directly from the approaches of World3, FOSSIL2, and STER (Gilliland, (Gilliland, 1978). 1978). In particular, following STER, it is designed to determ determine ine the maximu maximum m growth growth potentia potentiall of an econom economyy as a function of its resource constraints. The core ECCO model is a top-down aggregated simulation of a national economy. The structure of ECCO follows the Natural Capital Accounting methods of Slesser of Slesser (1989). (1989) . This basis leads to a strong emphasis on the e fficiency of primary resource production, and therefore emphasis on EROEI. The model de �nes three types of natural capital, depletable (e.g. fossil fuels), recyclable (e.g. aluminum) and renewable (e.g. solar power). Similar to STER, ECCO is classed as a biophysical model and describes all the interactions in the economy in terms of energy. Following Dürr (1994), (1994) , ECCO has a novel production function with factors in terms of operational energy and capital-embedded energy. The ECCO production function interestingly neglects the role of labour. Further, ECCO explicitly calculates the EROEI of multiple primary resources, including energy resources. ECCO models typically typically incorporate incorporate an aggregated aggregated world model, referred to as CORECCO or GlobECCO. A diagram of the CORECCO structure is shown in Fig. 9. 9. Similar to the ECCO national models,
2.3. Recent models (2000 – 2017)
Following from STER and ECCO, the Global Energy Modelling – a Biophysical Approach model (GEMBA; Dale et al., 2012) 2012 ) is a recent direct eff ort ort to explore EROEI dynamics over the renewable energy transition. GEMBA’s description description of EROEI-dynamic EROEI-dynamicss is highly highly intuitive and engaging. Furthermore, GEMBA is notably well constructed for calibration with empirical data. GEMBA can be technically described as a globally aggregated topdown, biophysical biophysical model that utilises utilises a highly highly simpli�ed economy economy model with a production function in terms of capital and a capital to output ratio (Fig. (Fig. 11). 11). In particular, GEMBA simulates a detailed range of production technologies, including coal, oil, gas, wind, hydro and solar. GEMBA estimates EROEI in terms of resource depletion and technological development (Dale (Dale et al., 2010). 2010 ). The main results of the GEMBA model suggest that the relative capital requirement of the energy sector may increase over the energy transition to around half the capital in the economy (Fig. ( Fig. 12a; 12a; similar to STER and the Sterman model). Over the same period, the total available 264
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1992 ) taken from Dale (2010). (2010) . Fig. 9. Outline of CORECCO ( Slesser, 1992)
Fig. 10. A schematic of the FREE model, taken from Fidderman (1997).
energy energy is suggested to peak mid-21st century, eventually eventually stabilising stabilising at around half current values in 2200 (Fig. ( Fig. 12b). 12b). Dale et al. (2013) compare the MESSAGE MESSAGE (common (common Integrated Integrated Assessment Model) projections, associated with the Special Report on
Energy Scenarios (SRES; IPCC) with the GEMBA projections. GEMBA ’s projections projections provide lower future estimates estimates of energy energy availability availability then MESSAGE. It is argued that this is because GEMBA utilises more realistic primary resource estimates. 265
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2013 ). Fig. 11. A schematic of the GEMBA model ( Dale et al., 2013).
Finally, a recent work by Brandt (2017) focuses (2017) focuses on the dynamics of the ‘Net Energy Cli ff ’. The Brandt approach is novel for the distinction that it makes between GDP and ‘ material prosperity ’. This emphasis on ‘material prosperity ’ provides a strong link with the Biophysical approach, however, the Brandt model is not biophysical. The Brandt model is a bottom-up globally aggregated model that uses a neoclassical production function, with factors in, capital, labour, energy and resources. The economy component of the Brandt model is derived from the KLEMS economy model ( Van Ark et al., 2007) 2007 ) and its representation of energy production uses an input-output model of the energy energy industry. industry. The Brandt model explicitly explicitly simulates simulates EROEI-dyEROEI-dynamics, where a decline in the EROEI of production is shown to drive a decline decline in the material prosperity prosperity of the economy. The results of Brandt Brandt (2017) are used to explore the concept of a minimum EROEI. As suggested by Hall and Murphy and Murphy (2011) Brandt (2011) Brandt �nds a consistent behaviour in all runs, where below a speci �c level of
EROEI, typically ~7, the material wealth of the economy declines rapidly. 3. Resource Resource curve methods
In addition to the EROEI-dynamics models, a related body of literature utilises resource curve methods (e.g. Hubbert, 1956) 1956) to explore 21st century energy futures. Studies within this group include: Brecha (2008),, Doose (2004), (2008) (2004) , Nel and Cooper (2009), (2009) , Macías and MatillaGarcía (2015). (2015) . Resource curve methods simulate resource depletion but do not explicitly include EROEI-dynamics. The approach uses empirical observations of resource depletion and global geological data to estimate the future global depletion of resources. Resource curve methods typically apply these distributions as exogenous constraints. The resource curve literature is extensive and often overlaps with EROEI-dynamics models, it includes some in �uential studies that are well cited
Fig. 12. GEMBA model output: Left (a): the distribution of capital between the energy sector and the industrial sector. Right (b): the projected energy production of the global economy (Dale ( Dale et al., 2013). 2013 ).
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three core pre-conditions. pre-conditions. First, carbon carbon emissions emissions must not surpass levels chosen chosen to limit global global warming to 2 °C (between (between 510-1505 Gt CO ; IPCC, 2014). 2014 ). Second, energy availability must not decline below levels deemed as unrealistic (between 600 and 6000 Watts per person). Third, the rate of transition to low-carbon technologies should not exceed that which fully depletes none-renewable resources. The results of the NETSET model suggest that there are a number of biophysically feasible pathways to transition to a low carbon economy (Fig. 15). 15). Sgouris Sgouris et al. (2016) uses (2016) uses a ‘ feasibility indicator’ to determine which pathways are most likely. It intuitively suggests that the transition becomes more di fficult if it is delayed, or if the carbon emission targets targets are relatively relatively stringent. However, the model does not simulate simulate macro-econom macro-economic ic variables. variables. The model, therefore, therefore, does not discuss the economic or social implications of the projected energy pathways. 2
Fig. Fig. 13. A comparison of a global carbon emissions scenario estimated by
Brecha (2008) and the IPCC Integrated Assesment Model scenarios. Brecha (2008) scenarios (2008) scenarios are shown by full lines, IPCC scenarios are shown by dashed lines.
4. Discussion Discussion
In the spirit of organising our discussion, it is useful to present a historical synthesis of the models discussed in this paper. An overview of the key literature literature is shown in Fig. 16 and 16 and a summary summary table is shown in Appendix I. I . Four model groups are evident: the MIT System Dynamics group (red), the US department of energy group (blue), the BPB group (green), and the STER-ECCO group (orange). These four groups can be additionally linked. The red and blue groups are interconnected through the work of authors such as Sterman and Fidderman. The green and orange groups show strong association both through common authors and model design. As previously stated, the World3 model is one of the earliest models to directly explore the inter-related global challenges associated with unsustainable 20th century growth trends, having a de �ning impact on following work. It is also perhaps one of the earliest models that implicitly plicitly simulates EROEI dynamics. It is important important to note that World3 follow followss World2 World2 and the formative formative work of Forest Forester er in Indust Industria riall Dynamics Dynamics (Forrester, 1970). 1970 ). Following World3, the development of EROEI models can be described in terms of the MIT (red-blue) group and the Biophysical (orange-green) group. A divergence between the MIT and Biophysical groups occurs around 1980 associated with the STER model and the Sterman model. These models are perhaps the earliest to directly simulate EROEI dynamics; both make signi �cant contributions to the literature. The Sterman model signi �es a more direct development development of World3 World3 but also draws in�uence from the COAL and FOSSIL models. The model has a stylised description of the energy sector and a more complex representation of the economy that provides a good platform for exploring energy-economy interactions. One may argue that many features of the Sterman model, such as the misallocation of capital, or the role of in�ation and unemployment have been insu fficiently explored following its publication. The Sterman model is followed by the FREE model, which integrates the Sterman approach with recent economic theory and explores a range of policy options. At a similar time to the Sterman model, the STER model introduces multiple multiple novel features to EROEI modelling, modelling, including including the biophysical approach. It is unfortunate that the STER model is only documented by a brief preliminary paper. STER is followed by a series of biophysical models: BPB, ECCO and GEMBA (green and yellow). The BPB model uses a similar similar approach to STER but explores a distinctive question on the role of EROEI in historical inter-decadal energy transitions. The ECCO framework develops the STER approach and applies it to a range of national economies, with a particular particular interest in understanding understanding the resource constraints of economic growth. Finally, GEMBA continues the approach of this group but returns to focus directly on the renewable energy transition and provides an intuitive structure that is well designed for calibration with empirical data. Of this group, the GEMBA model is perhaps the most directly interested in EROEI dynamics and provides a critique of the highly cited integrated assessment models
by the EROEI-dynamics EROEI-dynamics modelling modelling literature. literature. The following section outlines outlines a number of notable studies that use these methods. Following World3, Capellán-Pérez and collaborators (e.g. CapellánPérez et al., 2014; 2014 ; Mediavilla et al., 2013) 2013 ) developed the World Limits Model (WoLiM). WoLiM is a biophysical system dynamics model that uses resource curve methods. WoLiM is designed to explore the feasibility of 20th century growth trends (Fig. ( Fig. 13). 13). WoLiM is a bottom-up model with notable detail in primary energy production (Fig. (Fig. 14). 14). The model is driven primarily primarily by a group of GDP and population scenarios that are set externally (exogenous). Therefore EROEI decline is not able to in�uence GDP and the model is not able to properly simulate EROEI dynamics. The model simulates the depletion of resources with resource depletion curves (e.g. Mohr, 2012, 2012 , Patzek and Croft, 2010). 2010 ). WoLiM’s results typically predict resource constraints in the early half of the 21st century. The majority of WoLiM runs exceed environmenta environmentall limits leading to dangerous dangerous climate change. WoLiM maybe be thought of as a model that is designed to test for resource limitations limitations as a result of optimistic 21st century century growth growth projections. projections. Kumhof and Muir (2014) and Benes et al. (2015) provide related modelling studies that are more closely aligned with resource curve models models then then with the EROEI EROEI dynami dynamics cs models models.. Kumhof Kumhof and Muir (2014) use (2014) use a top-down, Dynamic Stochastic General Equilibrium Model to explore the behaviour of the global economy responding to a decline in oil production. The model simulates energy scarcity using a resource depletion curve, which is expressed in terms of the oil price. Benes et al. (2015) use (2015) use an econometric model of the global oil market to explore the response of the economy to a decline in oil production. The Benes model simulates EROEI dynamics through a rising oil price, associated with a depletion curve. The Kumhof model suggests that a realistic decline in the global oil production may drive a signi �cant decline in the GDP growth rate of both importer and exporter countries. This decline decline is notable since the growth rates of the high GDP countries countries are currently declining and additional decline may constitute stagnation or decline (Summers 2011). The Benes model similarly predicts that resource depletion with drive oil prices to almost double over the coming decade. Benes notes that this scale of increase would have a dramatic impact on the global GDP, further supporting the arguments of Kumhof of Kumhof and Muir (2014) and (2014) and the EROEI dynamics models. Finally, Sgouris Finally, Sgouris et al. (2016) developed the NETSET resource curve model. The NETSET model uses a novel inverse approach to simulate a range of feasible transition pathways. The framing of the approach is well illustrated by the title: ‘… quantifying the narrowing net-energy pathways to a global energy transition ’ (Sgouri ( Sgouriss et al., 2016 2016). ). The NETSET model is a globally aggregated, top-down, biophysical simulation model. The model uses an exogenous projection of future population to estimate future energy demand. It then integrates backwards to estimate a range of transition pathways that satisfy emissions targets and minimum energy per capita requirements. NETSET uses 267
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2014 ) IB stands for Industrial Buildings sector. The WoLiM structure can be Fig. 14. Schematic illustration of the structure of the WoLiM model ( Capellan-Perez et al., 2014) described as follows: GDP growth drives energy demand. Energy demand is divided into three categories. Demand drives production through a number of technology options. The optimal pathway for generation is a function of resource limits and environmental pollution.
that appear to neglect EROEI dynamics. Following the progression of the MIT, and the biophysical modelling groups, a number of relatively independent models have made signi�cant contributions. For example, WoLiM provides an alternative approach to the problem outlined by World3. It utilises more recent approaches to economic theory and highlights that current resource consumption growth trends are unsustainable, both in terms of resource and pollution limits. Despite the diversity in the EROEI-dynamics literature, there are seven key arguments or dynamics that run through all material. These dynamics are listed below, along with the dates of their �rst documentation.
requires increasing resources (including energy) to maintain supply with with dema demand nd (Hou Hounam nam,, 197 1979; 9; Ste Sterma rman, n, 198 1982; 2; Sle Slesse sser, r, 199 1992; 2; Fiddaman, 1997; 1997; Dale, 2010; 2010; Capellán-Pérez et al., 2014; 2014 ; Kumhof and Muir, 2014; 2014 ; Benes, 2015). 2015 ). Short-term rm confusion confusion - during 3. Short-te during the transi transition tion to low-ca low-carbo rbonn techtechnology, a short-term misallocation of capital and labour occurs due to imperfect information (Sterman, (Sterman, 1982; Fiddaman, 1997). 1997 ). 4. Short-term over production - energy scarcity drives both an increase in energy e fficiency and investment in energy production. Sterman (1982) argues that this leads to a short-term overproduction of primary energy. 5. Short-term scarcity - a decline in EROEI is expected to drive scarcity and in�ation (Sterman, (Sterman, 1982). 1982). 6. Energy transition ‘ long long wave’ - ‘long wave’ (inter-decadal) economic variability may be associated with energy transitions and EROEI (Baines and Peet, 1983). 1983 ). 7. ‘ Net Energy Cli ff ff ’ ’ - economic systems become unstable as EROEI declines declines below 10:1 associated associated with the ‘Net Energy Cli ff ’ (Brandt, ( Brandt, 2017; Murphy and Hall, 2010 ).
1. EROEI declines during the low carbon transition – during the transition to low carbon technology, primary energy production becomes less efficient (this is �rst implied by Meadows et al., 1972; this is fundamental damental to all models in this review except the BPB model). model). 2. The energy sector outgrows the economy (aka. energy cannibalism) - as the EROEI of primary energy production declines, the energy sector 268
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Capellán-Pérez et al., 2014 ). This behaviour is similar to the ‘system collapse’ described by World3 and CORECCO ( Meadows et al., 1972; Slesser, 1992). 1992 ). However, models such as the Sterman (1982) and (1982) and Fidderman (1997) predict short-term instability instead of long-run decline. Here, it is important to account for model design factors and consider the research questions being explored by given papers. For example, the Sterman model is highly idealised. It is not designed to predict future energy availability from empirical data. The models that are more directly designed to explore empirical estimates of resource constraints such as CORECCO, GEMBA and WoLiM typically predict a long long-r -run un decl declin inee in ener energy gy avai availa labil bility ity in the the mid mid 21st 21st cent centur uryy (Meadows et al., 1972; Slesser, 1992 ; Dale, 2013; 2013; Capellán-Pérez et al., 2014). 2014 ). Here it is argued that this issue requires further direct direct analyses of model code and empirical data. Following models such as GEMBA and WoLiM, multiple authors have expressed concern that the highly cited Integrated Assessment Modelling or Energy-Economy modelling literature does not e ff ectively ectively simulat simulatee or discuss discuss EROEIEROEI-dyna dynamics mics (e.g. (e.g. Dale Dale et al al., ., 20 2013 13)) and and therefore presents optimistic scenarios for future energy availability. Moreover, Keepin Moreover, Keepin and Wynne (1984), (1984) , Schneider (1997) and (1997) and Schneider Schneider and Lane (2005) argue that the subcomponents of Integrated Assessment Models are often poorly coupled suggesting EROEI dynamics are poorly simulated. This concern is also crucial, requiring directed research and review. It is argued that it is beyond the scope of this review. The Energy-Economy modelling literature is critiqued in general for docume documentat ntation ion issues issues (Pin Pindy dyck ck,, 20 2015 15;; Be Beck ck an andd Kr Krue uege ger, r, 20 2016 16;; Pfenninger, 2017; Schneider, 1997). 1997 ). It is argued that the EROEI-dynamics literature could also bene �t from improved documentation and archiving. For example, research papers (and/or manuals) for COAL, FOSSIL and STER are not readily accessible. However, these models are highly cited by later work. Model descriptions, terminology and schematics are often used inconsistently. For example, common challenging terms include, include, ‘Top-Down’, ‘Bottom-Up’, ‘Hybrid’, ‘Simulation’, ‘Optimisation’ and ‘General Equilibrium’. This issue is compounded by the concern concern that model code is rarely available for scrutiny, even decades decades after original research publications. It is further suggested that the literature may bene �t greatly from an improved e ff ort ort in review and synthesis. Further research in energy-economy dynamics is vital, considering the urgency of the transition to a sustainable economy (IPCC, ( IPCC, 2014; Sverdrup et al., 2015). 2015 ). Particularly considering that the current economic climate is characterised by declining economic growth rates (Summers, 2013), 2013), increasing instability (Rye ( Rye and Jackson, 2016) 2016 ) and increasing increasing uncertainty uncertainty (Baker et al., 2016), 2016 ), all of which are plausibly related to EROEI dynamics. There is therefore a notable requirement for further empirical studies in EROEI-dynamics. For example, it may be possible to � nd signatures of EROEI dynamics in Secular Stagnation, or in international trade. Further work is required to provide an improved link between empirical empirical work, such as Heun as Heun and de Wit (2012) and (2012) and the modelling literature. There are multiple a-priori EROEI dynamics identi �ed by our discussion that are not discussed by the current literature. These provide interesting subjects for further research. Examples of these dynamics include:
Fig. 15. An example of a NETSET simulated energy future, providing 2000 W
average net power per capita by 2100 to a population of 10.8 billion. Fossil fuel emissions do not exceed 990 GtCO . The dashed dashed line represents represents the net available energy. Values above this line represent energy invested. 2
models. Colours denote studies with the Fig. 16. The recent history of EROEI models. same or related authors. Relatively independent models are black. Full line: strong link between authors. Dotted line: signi �cant in�uence from a paper.
These These dynami dynamics cs may be useful usefully ly re-fra re-framed med into three groups: groups: Scarcity Scarcity (1, 2, 6), Instability Instability (5, 6, 7) and Uncertainty Uncertainty (3, 4). From this view, ‘ Scarcity’ encompasses the driving dynamics that lead to a decline in resource availability. Dynamics 1 and 2 of this group are the most consistent across the literature. ‘Instability’ encompasses potential systemic failures where systems are forced to sustain unanticipated resource constraints. Finally, ‘Uncertainty’ encompasses the period of change or adjustment, adjustment, where unavoidable unavoidable unknowns (imperfect (imperfect information) lead to the misallocation of capital. Scarcity is the primary driver of Instability Instability and Uncertainty, Uncertainty, however, however, these dynamics are inter-related and constitute feedbacks that are di fficult to simulate. From this framework, the issues of Instability and Uncertainty in energy-economy dynamics are notably poorly understood. The issue of instability is an interesting topic of divergence in the literature. Multiple authors, particularly in the MIT group, suggest that the low carbon carbon transitio transitionn is likely likely to drive drive a period period of instabil instability ity (Sterman, 1982, 1982, Fiddaman, 1997). 1997 ). However, dedicated modelling studies such as Jackson as Jackson and Victor (2015), (2015) , Jackson (2017), (2017), suggest that the transition to a steady state or low carbon economy does not necessitate instability. Further work is required to explore the role of EROEI in systemic instability and the macro policy levers available to facilitate a stable energy transition. In addition, the issue of late 21st century energy availability is an important topic in the literature. Models such as GEMBA and WoLiM predict a mid 21st century decline in energy availability (Dale, ( Dale, 2013; 2013;
• The role of EROEI in
�nancial instability – e.g. As EROEI declines currently highly valued resources may become unpro �table to extract. A sudden loss in value would provide a signi �cant destabilising perturbation/s perturbation/s to �nancial nancial systems systems (e.g. stranded assets: Carbon Tracker, 2013). 2013 ). The role role of EROEI EROEI in Secul Secular ar Stagna Stagnatio tion n – Following Following e.g. Slesser • The (1992),, EROEI plays a signi �cant role in determining the growth (1992) rate of an economy. Therefore, the decline in EROEI over recent decades may have played a primary role in Secular Stagnation.
• The role of international trade in global EROEI and global energy prices – Resource exploitation and international trade strategies are likely to 269
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change as EROEI declines and the nation state ’s concerns of resource scarcity increase. It is possible that changes in trade may lead to a decline decline in oil price during a period period of declining declining EROEI.
Greater research is required to explore this concern. Regardless Regardless of this concern concern many of the immediate-term immediate-term policy implications plications are consistent throughout throughout the literature. literature. These suggest that rapid investment in renewable energy and energy e fficiency are required, some form of carbon or resource tax would be bene �cial and delayed delayed actions are likely to increase increase the chance of dangerous climate chan change ge.. Furt Furthe her, r, ther theree is a sign signii �cant cant risk risk of system systemic ic collap collapse. se. However, policy implications di ff er er in the medium to long-term, where the EROEI-dynamics literature often advocates a low-growth or steady state economy. economy. This is discussed both in terms of a mitigation mitigation strategy and a biophysical inevitability. It is clear that greater research is required to explore the dynamics of the steady state economy, as the EROEI-dynamics literature consistently projects that it may become a prominent feature of the current century.
5. Concluding Concluding remarks remarks
The literature discussed by this review is diverse and spans almost 50 years from the initial publications of the Limits to Growth study. It is possible possible to map the development development of the EROEI-dynamics EROEI-dynamics models over this period, to identify seminal works and provide a historical catalogue of blueprints for future work. Notable early works include: Meadows (1973), Hounam (1973), Hounam (1979) and Sterman and Sterman (1982). (1982). The consistency of the key arguments stemming from these papers and running through the EROEI-dynamics literature is strongly supportive of their validity. The most widely established arguments suggest that geological resource constraints will drive a relative expansion of the energy sector in the coming century, and a downward downward pressure pressure on material material prosperity prosperity (arguments 1, 2, 3 and 5). It is clear that considerable work is required to further our understanding standing of EROEI-dynamics. EROEI-dynamics. Perhaps the most signi�cant concern concern following this review is the divergence in the literature, where the widely cited Integrated Assessment Modelling literature poorly cites EROEI-d EROEI-dyna ynamics mics litera literature ture.. It is possib possible le that that better better integra integration tion of EROEI-dynamics could have a signi �cant impact on the discussion of energy futures and therefore a signi �cant impact on government policy.
Acknowledgements
The � nancial support of the Economic and Social Research Council for the Centre for the Understanding of Sustanable Prosperity (ESRC grant no: ES/M010163/ ES/M010163/1) 1) is gratefully gratefully acknowledged. acknowledged. We would also like to thank thank resear researche chers rs at the Centre Centre for the Underst Understand anding ing of Sustainable Prosperity (CUSP.ac.uk) for their support. We would like to thank Iñigo Capellán-Pé Capellán-Pérez, rez, Roberto Pasqualino, Pasqualino, Sarah Hafner Hafner and Ange Angela la Druck Druckma mann for for thei theirr insig insight htfu full comme comment ntss and and supp suppor ortt throughout.
Appendix Appendix I. Model Summary Summary Table
Summary Summary of models discussed by the review. The models are classi classi �ed by the year of their � rst major publication as well as the development group assigned by this review. Red: MIT group. Blue: US government group. Orange: STER-ECCO group. Green: BPB group. Grey: no clear group. The models are also classi �ed by the key dynamics. Further, by novel conclusions and their earliest reference.
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